A nurture scientist to re-activate & convert cold leads
Gokul Anantha
It all began with the question – Where in the B2B revenue marketing value chain can AI create the greatest outcome impact ? How?
Conversations with marketing leaders pointed to 360-degree gaps . I captured it in an article here
https://loglens.io/blogs/scaling-b2b-revenue-marketing-outcomes-with-ai-and-ml/
Insights uncovered through these dialogues led me to a massive “nurture” gap. Consider this – 92 % of GTM leaders rated their nurture contributions as “unknown” and felt the program needs improvement. And there is more
Before we dive further; a bit about me . I was trained as an industrial engineer, tested in sales, and refined through product. That mix taught me to see systems, spot friction, and design for outcomes — not just output. At SAP, for example, this mindset helped me transform an SMS service into an intelligent API led service dramatically scaling customer acquisition outcomes. You can read more on the product here and all the related articles here
My time at SAP deepened my collaboration with marketing leaders , sparking a lasting passion for revenue marketing.
Mapping conversion journey as a value stream and discovering / eliminating “process losses” (e.g., MQLs stalling) is classic industrial engineering thinking applied to marketing ops.
I pushed the idea further – By applying predictive science to uncover patterns for e.g. I sought to uncover if there meaningful signals that indicate why some leads convert while others stall? And more importantly, could a system be built around those signals?
That line of thinking led me to a segment that most teams ignore: cold and inactive leads. These leads represent hidden pipeline potential — and yet, 7 out of 10 organizations do nothing with them. The problem wasn’t just that they’d gone cold. It was that there was no intelligent way to restart the conversation.
That seeded the concept of a virtuous nurture cycle. Instead of relying on static drip campaigns or assumptions about buyer behavior, this system listens, learns, and adapts. It uses AI to identify the best message, channel, and cadence for each lead, optimizing every touchpoint over time. And it gets smarter with every cycle .
Building this needs a data foundation. For more than a year now , we have also been building a “super easy to get started” lakehouse – we called it the GTM 360 project . Here is the conceptual view of how we went about it.
GTM 360 and an ML layer on it is now the foundation for the nurture AI infrastructure that we seek to transform into your AI edge in-a-box.
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